Dashing through the Snow (Autonomously)

Subscribe for Updates

Join the Transportation Tech mailing list today and get a free copy of our white paper submitted to ITS World Congress!

With success stories and headlines coming out seemingly every week from autonomous vehicle stakeholders like Waymo and Uber, it’s hard to imagine that there are still major technological challenges to hurdle before self-driving cars will dominate the landscape.

While cities like Phoenix and San Francisco seem to have a clear path to a self-driving utopia, other areas, particularly those visited annually by freezing precipitates—say, Milwaukee in December—might not have it so easy.

AVs navigate their environment using data and images collected from an array on-board cameras and sensory equipment. For example, vehicle cameras keep track of pavement markings to help the car stay on the road and in the lane. But what happens when the pavement—or even the camera—becomes covered in snow?

Or, take LIDAR, which is one of the most prominent sensor types used in AVs today. LIDAR shoots thousands of laser beams in all directions, then uses the bounce-back data to generate a three-dimensional picture of the car’s surrounding environment. It’s kind of like how bats use sonar to navigate their way out of a cave. LIDAR tends to become confused, however, in adverse weather conditions. When it bounces off a snowflake, for example, it might think the snowflake is an obstacle directly in front of the car.

Companies have been working to perfect the capabilities of AVs in wintry conditions for quite some time—which is exactly why Waymo began testing its AVs in Michigan in 2017. But such tests occur in highly controlled conditions, and have yet to prove that AVs are ready for their Arctic debut. Until now, perhaps.

Finland’s VTT Technical Research Center announced a breakthrough this week when Martti—one of its autonomous test vehicles built for snow and icy conditions—successfully navigated a fully snow-covered public road, lacking visible pavement markings, at speeds up to 25 miles per hour. This was the first time, VTT believes, that any autonomous vehicle has demonstrated this level of ability in real-world snow conditions, particularly at this speed.

Martti is special because its navigation system relies heavily not on LIDAR, but on radar, which is capable of penetrating snow, fog and rain. Martti’s sensors and algorithms were also optimized for specifically these unique conditions. Martti’s counterpart, Lydia, on the other hand, is more suited for urban driving applications. VTT says next year they plan to build a third AV that’s ideal for forested, and even tougher, terrains. The point of these test vehicles, VTT says, is not to make a perfect consumer car for snowy weather, but to test the limits of AV technology and expand its horizons.

While it may be too soon to say whether Martti’s success signals the impending end of weather-related trouble for AVs, it’s a definitely step in the right direction.